Free SOA Exam PA (Predictive Analytics) Predictive Analytics Problem Definition Practice Questions

Problem definition on SOA Exam PA covers translating a business problem into a predictive modeling task: choosing the target variable, framing regression versus classification, identifying constraints and stakeholders, and stating how model results will drive a business decision.

75 Questions

20 Easy

48 Medium

7 Hard

2026 Syllabus

Sample Questions

Question 1 Easy

Which of the following business requests is best matched to PREDICTIVE analytics rather than descriptive or prescriptive?

Solution

D is correct. Estimating each borrower's future default likelihood is a forward-looking outcome estimate, the defining feature of predictive analytics. Ranking and reporting prior-year figures are descriptive, while choosing a cutoff or allocating a budget to optimize an objective are prescriptive.

Question 2 Medium

Which of the following pairs a business goal with the type of analytics it primarily represents CORRECTLY?

Solution

D is correct. Forecasting a future, per-customer claim count is a predictive task because it estimates an unknown future outcome. Estimating a future lapse rate is predictive (not descriptive), reporting last year's loss ratios and tabulating prior-year premiums are descriptive (not predictive/prescriptive), and choosing a discount to maximize retention is prescriptive (not descriptive).

Question 3 Hard

A team can increase the size of the training dataset for an unchanged, moderately flexible GLM. Holding the model form fixed, what is the most accurate expected effect on the bias-variance decomposition?

Solution

D is correct. More training data stabilizes the estimated coefficients, so the fitted predictions vary less across samples — variance decreases. Bias is set by the chosen model form (a GLM still cannot capture structure it does not include), so it is largely unchanged, and the irreducible process noise

\mathrm{Var}(\varepsilon)

is a property of the data, not the sample size.

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